Head-to-head showdown: NVIDIA Llama 3.1 Nemotron 70B ($0.35 in / $0.70 out per 1M) vs GPT-5.5 Pro ($30.00 in / $180.00 out per 1M). NVIDIA Llama 3.1 Nemotron 70B is 200.0× cheaper across standard token mixes, with 128K vs 512K context windows.
nvidia/llama-3.1-nemotron-70b-instruct · nvidia
gpt-5.5-pro · openai
Benchmark
| Workload Scenario | NVIDIA Llama 3.1 Nemotron 70B | GPT-5.5 Pro | Price Delta |
|---|---|---|---|
| 1M input tokens (raw text) | $0.35 | $30.00 | −$29.65 |
| 1M output tokens (generation) | $0.70 | $180.00 | −$179.30 |
| 1M tokens · 70% input / 30% output mix | $0.455 | $75.00 | −$74.545 |
| Standard chat turn (4K in / 800 out, 50% cached) | $0.00196 | $0.21 | −$0.208 |
| Monthly scale (10K requests / day) | $588.00 | $63,000.00 | −$62,412.00 |
Negative difference = NVIDIA Llama 3.1 Nemotron 70B is cheaper. Positive = GPT-5.5 Pro is cheaper.
Scaling Curve
Total cost of a token volume at 70/30 input/output split (uncached). Log-log scale.
Verdict
NVIDIA Llama 3.1 Nemotron 70B is cheaper on both input and output rates, so it costs less at every input/output mix. Price alone still isn't the whole decision: capability, latency and context limits (NVIDIA Llama 3.1 Nemotron 70B: 128K, GPT-5.5 Pro: 512K) may justify the premium for your task.
FAQ
On input, NVIDIA Llama 3.1 Nemotron 70B is cheaper ($0.35/M vs $30.00/M). On output, NVIDIA Llama 3.1 Nemotron 70B is cheaper ($0.70/M vs $180.00/M). The same model is cheaper on both sides, so it wins at every mix.
A chat-style request (4,000 input + 800 output tokens, 50% cached) costs $0.00196 on NVIDIA Llama 3.1 Nemotron 70B and $0.21 on GPT-5.5 Pro — NVIDIA Llama 3.1 Nemotron 70B is 107.1× cheaper for that workload.
NVIDIA Llama 3.1 Nemotron 70B supports 128,000 tokens (8.2K max output); GPT-5.5 Pro supports 512,000 (64K max output). GPT-5.5 Pro fits 4.0× more context, which matters for long documents and agents.